Identifying related cancer types based on their incidence among people with multiple cancers
Bibliographic record
Abstract
BACKGROUND: There are several reasons that someone might be diagnosed with more than one primary cancer. The aim of this analysis was to determine combinations of cancer types that occur more often than expected. The expected values in previous analyses are based on age-and-gender-adjusted risks in the population. However, if cancer in people with multiple primaries is somehow different than cancer in people with a single primary, then the expected numbers should not be based on all diagnoses in the population. METHODS: In people with two or more cancer types, the probability that a specific type is diagnosed was determined as the number of diagnoses for that cancer type divided by the total number of cancer diagnoses. If two types of cancer occur independently of one another, then the probability that someone will develop both cancers by chance is the product of the individual probabilities for each type. The expected number of people with both cancers is the number of people at risk multiplied by the separate probabilities for each cancer. We performed the analysis on records of cancer diagnoses in British Columbia, Canada between 1970 and 2004. RESULTS: There were 28,159 people with records of multiple primary cancers between 1970 and 2004, including 1,492 people with between three and seven diagnoses. Among both men and women, the combinations of esophageal cancer with melanoma, and kidney cancer with oral cancer, are observed more than twice as often as expected. CONCLUSION: Our analysis suggests there are several pairs of primary cancers that might be related by a shared etiological factor. We think that our method is more appropriate than others when multiple diagnoses of primary cancer are unlikely to be the result of therapeutic or diagnostic procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".